Maps and Resource Data

Resource maps and data can fill critical needs at several stages in the development of a solar or wind project. They can support site prospecting and early-stage feasibility studies, help evaluate alternative project sites, and provide the basis for preliminary or advanced plant design and energy production estimates. UL is a world leader in the science and application of resource mapping and modeling. Our emphasis on high-resolution modeling using the most advanced methods, along with extensive validation, means you can rely on us for your mapping and data needs.

Micrositing maps and resource grids to support plant design and energy estimates. Access the same advanced resource modeling we use to design optimal layouts and perform bankable energy estimates. (Onsite measurements are required for the highest accuracy.) GIS, CSV, and WRG/WRB formats.

Our methods

All of our resource maps and data are derived from simulations of historical atmospheric conditions performed by a numerical weather prediction (NWP) model. Typically, the results of the simulations drive a microscale model, which accounts for the local influences of terrain and surface conditions. Finally, where appropriate, the model output is fine-tuned using available high-quality wind measurements. Our approach has been thoroughly validated, and uncertainty statistics are available. See our Wind Resource Maps and Data Methods and Validation for details.

Preliminary project design and evaluation. Download a typical-year or long-term VMM and WRG file and import them to Openwind, our plant design software. Select turbine models, create plant layouts, and estimate energy production and wake losses – even before you take measurements.

Wind monitoring program design. Choose locations for prospective wind monitoring stations. Take your maps into the field while you scout monitoring sites.

Advanced project design and evaluation. With on-site measurements, you can carry out advanced resource assessment, plant design, and energy production estimates. Use our VMMs for MCP climate adjustments, as well as the Openwind software to adjust the wind resource grids to your measurements, calculate losses, and estimate energy production.

Let us be part of your team. We can work with your team in the way that makes most sense for you – either in a consulting mode or by providing resource data, online services, and advanced software enabling your team to do the work itself. By providing support in these different ways, we can maximize your team’s effectiveness while minimizing your overall development cost and schedule.

Publicly available Typical Meteorological Year (TMY) datasets are satellite modeled and can have biases of up to 25% on a monthly basis. A more rigorous review of the data available through national and regional networks is recommended.

Our methods

A site specific TMY data set will be constructed to accurately characterize the solar resource potential, as well as to define its seasonal and diurnal distributions. The resulting dataset has seasonal and annual averages comparable to the long-term and will represent typical conditions at the site.

Benefits and applications

The resulting dataset can be imported into energy modeling software and can be used as a direct substitute for publicly available TMY datasets. The benefit of our Advanced TMY is that it provides data that is custom to your exact project location. Many of the publicly available datasets are for locations that can be miles away from your actual project. Additionally many of these datasets are constructed using a variety of methods many of which do not include actual ground based measurements. This can greatly impact the resulting TMY values and cause for inconsistent results across very short distances.

UL constructs its datasets using a combination of regional ground based solar irradiation measurements as well as satellite modeled datasets to produce results that represent the conditions at your project location and not miles away. Our Advanced TMY can be used as a direct input into energy models and can help provide a more accurate assessment of the long term energy output when compared to publicly available data.